Evolution of Artificial Neural Networks Using a Two-dimensional Representation

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@PhdThesis{pujol:thesis,

• author = "Joao Carlos Figueira Pujol",
• title = "Evolution of Artificial Neural Networks Using a Two-dimensional Representation",
• school = "School of Computer Science, University of Birmingham",
• year = "1999",
• address = "UK",
• month = apr,
• email = "pujol@urano.cdtn.br",
• keywords = "genetic algorithms, genetic programming, PDGP",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/PhD_Thesis_pujol.pdf",
• size = "178 pages",
• abstract = "The design of artificial neural networks is still largely performed by an expert, with only a few heuristics to guide a trial-and-error search. Recently, new methods based on evolutionary computation (EC) have been applied to the synthesis of artificial neural networks with modest results. The basic limitation of EC-based methods is that they do not take into account the fact that artificial neural networks are two-dimensional structures, and do not use specialized evolutionary operators. In this work, a new method based on a special form of evolutionary computation called genetic algorithms is proposed for the evolution of artificial neural networks. The method is a general purpose procedure able to evolve feedforward and recurrent architectures. It is based on a two-dimensional representation, and includes operators to evolve the architecture and the connection weights simultaneously. The new approach has shown promising results, and has fared better than previous methods in a number of applications, including: binary classification problems, design of neural controllers and a complex navigation task of traversing a trail. An extension of the two-dimensional representation is also presented, which can be combined with other methods, providing them with an alternative procedure to evolve the weights of the connections.",

}

Genetic Programming entries for Joao Carlos Figueira Pujol

Citations